Attanasio, A, Scaglioni, B orcid.org/0000-0003-4891-8411, Leonetti, M orcid.org/0000-0002-3831-2400 et al. (4 more authors) (2020) Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery – A Feasibility Study. IEEE Robotics and Automation Letters, 5 (4). pp. 6528-6535. ISSN 2377-3766
Abstract
In this letter, we describe a novel framework for planning and executing semi-autonomous tissue retraction in minimally invasive robotic surgery. The approach is aimed at removing tissue flaps or connective tissue from the surgical area autonomously, thus exposing the underlying anatomical structures. First, a deep neural network is used to analyse the endoscopic image and detect candidate tissue flaps obstructing the surgical field. A procedural algorithm for planning and executing the retraction gesture is then developed from extended discussions with clinicians. Experimental validation, carried out on a DaVinci Research Kit, shows an average 25% increase of the visible background after retraction. Another significant contribution of this letter is a dataset containing 1,080 labelled surgical stereo images and the associated depth maps, representing tissue flaps in different scenarios. The work described in this letter is a fundamental step towards the autonomous execution of tissue retraction, and the first example of simultaneous use of deep learning and procedural algorithms. The same framework could be applied to a wide range of autonomous tasks, such as debridement and placement of laparoscopic clips.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Surgery, Robots, Task analysis, Tools, Planning, Laparoscopes, Instruments |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society wm150122 EPSRC (Engineering and Physical Sciences Research Council) EP/R045291/1 Intuitive Surgical Inc Not Known EU - European Union 818045 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jul 2020 15:05 |
Last Modified: | 22 Sep 2020 14:52 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/LRA.2020.3013914 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163725 |